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Articles 871 - 900 of 13783

Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering

Real-Time Decision-Making During Maritime Chokepoint Disruption Using Digital Twin, Jared M. Orendorff Mar 2025

Real-Time Decision-Making During Maritime Chokepoint Disruption Using Digital Twin, Jared M. Orendorff

Theses and Dissertations

This research develops a digital twin of the global maritime shipping system to model disruptions in major shipping lanes like the Suez and Panama Canals. By incorporating live ship-tracking data, the model simulates closures, forecasts queue lengths, and determines the best rerouting options. Findings show that canal closures cause large traffic backlogs and increased congestion at alternative chokepoints, while rerouted ships may face higher piracy risks in regions like the Gulf of Guinea and the Strait of Malacca. This tool helps decision-makers respond effectively to maritime disruptions.

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Accuracy Of Time Phasing Missile And Munition Using The Continuous Distribution Function, Joseph Zobler Mar 2025

Accuracy Of Time Phasing Missile And Munition Using The Continuous Distribution Function, Joseph Zobler

Theses and Dissertations

Accurate cost and schedule estimates are crucial for maintaining the U.S. military’s technological and operational superiority, ensuring efficient resource allocation and timely development of advanced defense systems. This research examines S-curve models for time-phasing non-recurring Research, Development, Test, and Evaluation (RDT&E) expenditures in missile and munition acquisition programs. This research evaluates the commonly used 60/40 rule, which assumes 60% of expenditures occur by 50% of the schedule, for its accuracy using Cost Assessment Data Enterprise (CADE) and Earned Value Management Central Repository (EVM-CR) data from 21 missile and munition development programs.

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A Reinforcement Learning Approach For Maneuvering And Firing Decisions In Sead Operations, Nathaniel Garcia Mar 2025

A Reinforcement Learning Approach For Maneuvering And Firing Decisions In Sead Operations, Nathaniel Garcia

Theses and Dissertations

The integration of automated processes in defense continues to expand, enhancing the lethality of military forces. Artificial intelligence accelerates decision-making cycles, removes the constraints of human-operated hardware, and improves coordination by enabling seamless integration across multiple systems. Suppression of Enemy Air Defenses (SEAD) missions are critical to the United States (U.S.) military, as they neutralize hostile air defense systems, ensuring air superiority and enabling safe and effective operations for aircraft in contested environments. Therefore, it is necessary to pair emerging autonomous capabilities with an important mission set in defense. This research investigates the Autonomous Unmanned Air-to-Ground Strike (AUAGS) problem, modeling …

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Symbology Detection And Numerical Recognition For T-38 Heads-Up Display Recordings, Ben T. Hepner Mar 2025

Symbology Detection And Numerical Recognition For T-38 Heads-Up Display Recordings, Ben T. Hepner

Theses and Dissertations

The extraction of symbology and numerical data from the T-38 Heads-Up Display (HUD) enhances post-flight analysis and supports real-time decision-making. This research develops a deep learning pipeline using YOLO-based object detection and Optical Character Recognition (OCR) to analyze HUD video data. Model evaluations showed mAP0.5:0.95 ranging from 0.422 (YOLOv11m, hard test set) to 0.696 (YOLOv8m, medium test set), demonstrating robust symbology detection. Numeric detection performed well (mAP0.5:0.95 = 0.764), but OCR struggled with glare and resolution limitations, achieving a recognition accuracy of 17.35%. These results validate deep learning for HUD data extraction but highlight the need for improved robustness …

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Weighed And Measured: Toward An Evaluation Method For Digital Models In Source Selection, Liam N. O'Neill Mar 2025

Weighed And Measured: Toward An Evaluation Method For Digital Models In Source Selection, Liam N. O'Neill

Theses and Dissertations

Inspired by a recent model-based source selection conducted by the Advanced Range Threat System (ARTS) Program Office at Hill AFB, this research effort explored the development of new tools the DoD could use when evaluating models submitted with proposals. Specifically, the effort aimed to incorporate the Multi-Objective Decisions Analysis (MODA) framework into SysML diagrams as a solution for technical evaluations on models submitted with offeror proposals, eventually producing the Model-Based Decision Tool (MBDT). The MBDT is built from a Value Hierarchy based on key system requirements, each weighted by priority and measured by their own Single-Dimensional Value Functions (SDVFs). By …

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Dartbot: Overhand Throwing Of Deformable Objects With Tactile Sensing And Reinforcement Learning, Shoaib Aslam, Krish Kumar, Pokuang Zhou, Hongyu Yu, Michael Yu Wang, Yu She Mar 2025

Dartbot: Overhand Throwing Of Deformable Objects With Tactile Sensing And Reinforcement Learning, Shoaib Aslam, Krish Kumar, Pokuang Zhou, Hongyu Yu, Michael Yu Wang, Yu She

School of Industrial Engineering Faculty Publications

Object transfer through throwing is a classic dynamic manipulation task that necessitates precise control and perception capabilities. However, developing dynamic models for unstructured environments using analytical methods presents challenges. In this study, we present DartBot, a robot that integrates tactile exploration and reinforcement learning to achieve robust throwing skills for nonrigid relatively small objects under the influence of moment of inertia which cause the object to spin in the air. Unlike traditional sim-to-real transfer methods, our approach involves direct training of the agent on a real hardware robot equipped with a high-resolution tactile sensor, enabling reinforced learning in a realistic …

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The Location Set Covering Disruption Problem, Richard A. Sheldon Mar 2025

The Location Set Covering Disruption Problem, Richard A. Sheldon

Theses and Dissertations

This research models and analyzes a variant of the Location Set Covering Problem (LSCP) in a bilevel, game theoretic setting by posing the LSCP as a non-cooperative attacker-defender Stackelberg game, where facilities are to be emplaced by the defender from a boarder set of potential facility locations to cover a set of demands; however, an attacker removes the possibility of emplacing q specific facility locations with the objective to remove the maximum weighted value demands, and then lexicographically maximize the cost of coverage of remaining demands. A novel methodology leveraging lexicographic programming computed an optimal solution for 98% of all …

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Island Nation Duress: Simulating Passive Peer-To-Peer Bluetooth Communication During Disaster Relief, Jason K. Medeiros Mar 2025

Island Nation Duress: Simulating Passive Peer-To-Peer Bluetooth Communication During Disaster Relief, Jason K. Medeiros

Theses and Dissertations

Pacific Islands under U.S. jurisdiction are highly vulnerable to natural disasters, yet many lack the infrastructure to effectively respond and recover. Clear communication during and after such events is critical for evacuation, hazard awareness, and first responders’ coordination. This research explores a simulation-based approach using Bluetooth communication to relay messages across Guam, assessing its efficiency through statistical analysis. By examining regional differences and geographic impacts on Bluetooth messaging, the study aims to identify key factors that enhance peer-to-peer communication for timely and effective disaster response.

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Safety-Driven Concept Development Using System Theoretic Process Analysis Extended For Coordination In A Model Based Environment: A Case Study On Autonomous Satellite Teaming, Taylor L. Matarazzo Mar 2025

Safety-Driven Concept Development Using System Theoretic Process Analysis Extended For Coordination In A Model Based Environment: A Case Study On Autonomous Satellite Teaming, Taylor L. Matarazzo

Theses and Dissertations

Rigorous system safety analysis methods, allow programs to identify potential problems helping to minimize their impact to program schedules and budgets. In the contested, congested, and competitive space environment, coordination within and between systems is critical to mission success. System Theoretic Process Analysis extended for Coordination (STPA-coord) can prescriptively analyze these coordination interactions. STPA-coord shifts the conversation of system safety from elements ofreliability to elements of control, providing insights that holistically analyze the system. As studies suggest, decisions made early in a systems design determine 80-86% of a programs final cost, therefore integrating system safety as early into design can …

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Model-Based Approach To Support Safety Driven Design And Satisfy Mil-Std-882e Requirements With Stpa Coordination: A Case History In Suas Defense Acquisition, Daniel A. Shea Mar 2025

Model-Based Approach To Support Safety Driven Design And Satisfy Mil-Std-882e Requirements With Stpa Coordination: A Case History In Suas Defense Acquisition, Daniel A. Shea

Theses and Dissertations

Increasingly complex defense systems that are routinely overbudget and behind schedule are driving digital engineering initiatives in the defense acquisition industry. MBSE offers a solution to counter this issue but the lack of guidance on how to implement it has led to significant experimentation. One MBSE area of interest is system safety. This research demonstrates how to conduct model-based Systems Theoretic Process Analysis (STPA) to meet the unique system safety process requirements from MIL-STD882E. Based in systems theory, STPA extended for coordination enables a safety-driven design process of complex systems. This research investigated conducting STPA in the SysML-RAAML modeling language …

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Are Emojis The New Words? A Sentiment Analysis Of Social Media Brand Conversations, Yashodhan Karulkar, Dev T. Vora, Siddharth Vaddepalli, Yash Thakur Mar 2025

Are Emojis The New Words? A Sentiment Analysis Of Social Media Brand Conversations, Yashodhan Karulkar, Dev T. Vora, Siddharth Vaddepalli, Yash Thakur

Journal of International Technology and Information Management

Emojis have become an increasingly important aspect of consumer-brand interactions in the Indian subcontinent. However, the impact of emoji use on brand image and mental health remains underexplored, particularly in emerging economies like India, where structured research on this topic is limited. To address this gap, the present study analyzes over 4,600 consumer tweets related to 19 prominent brands across eleven industries. Using VADER sentiment analysis, the research develops a metric to assess consumer sentiment and brand engagement in relation to emoji usage. The findings indicate that effective integration of emojis contributes to positive consumer sentiment and enhanced brand engagement. …

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Practical Estimation Of Action-Generation Mechanisms In Repeated Games, Vladimer Kellachow Iii Mar 2025

Practical Estimation Of Action-Generation Mechanisms In Repeated Games, Vladimer Kellachow Iii

Theses and Dissertations

The goal of this research is to gain insight into how players of a game learn their strategy during the course of repeated play. The study employs the Experience Weighted Attraction (EWA) model, developed by Dr. Colin F. Camerer and Dr. Teck-Hua Ho, as the foundational behavioral framework. Using historic observed strategy decisions, the parameter values that define an opponent’s learning process are updated using various inference methods.

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Machine Learning Techniques To Detect Anomalies In T-38 Flight Sensor Data, Sydney M. Wekamp Mar 2025

Machine Learning Techniques To Detect Anomalies In T-38 Flight Sensor Data, Sydney M. Wekamp

Theses and Dissertations

Accurate sensors are critical for ensuring the safety of aircrew. However, detecting faulty sensors remains a significant challenge for the Test Pilot School at Edwards Air Force Base in California. Current methods rely on either student pilots identifying anomalies or waiting for sensors to fail completely before repairs are made—an approach that lacks reliability and consistency. This research aims to address these shortcomings by implementing machine learning techniques to detect sensor faults proactively. To date, applying machine learning to a dataset of this size, encompassing numerous sensors on the same aircraft, is unprecedented. The project focuses on establishing strong baseline …

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Machine Learning With Flight Data Recorder Data For Flight Fuel Consumption Predictions, Adam C. Levandowski Mar 2025

Machine Learning With Flight Data Recorder Data For Flight Fuel Consumption Predictions, Adam C. Levandowski

Theses and Dissertations

This study applies advanced Machine Learning (ML) to Flight Data Recorder (FDR) data for fuel consumption predictions. It explores feature engineering, model selection, and Hyper-Parameter Optimization (HPO) across all flight phases. Baseline models like Ordinary Least Squares (OLS) regression, Multi- Layer Perceptrons (MLPs), and decision trees are compared to Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs) with Gated Recurrent Unit (GRU) layers, and XGBoost. Results analyze segmentation strategies, tailored features, and model performance. A counterfactual analysis compares ML models to operational fuel predictions, demonstrating their deployment potential. Findings establish a foundation for future ML-driven advancements in aviation fuel optimization.

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Performing Requirements Specification And Analysis Through Open Generative Pre-Trained Transformers, Harvey J. Hurst Mar 2025

Performing Requirements Specification And Analysis Through Open Generative Pre-Trained Transformers, Harvey J. Hurst

Theses and Dissertations

Every acquisition program begins with a requirement, and for those programs to succeed, robust requirements engineering (RE) must be implemented. RE encompasses eliciting, analyzing, specifying, and validating requirements—a critical process throughout a program's lifecycle. Despite its importance, RE faces challenges such as scope creep, ambiguity, redundancy, and inadequate automation support, often exacerbated by reliance on historical data. To address these issues, this thesis leverages advancements in Generative Technology, particularly large language models (LLMs) such as Generative Pre-Trained Transformers (GPTs). This research developed two GPT-based tools: the Single Requirement Analysis Tool and the Set of Requirements Analysis Tool. These tools were …

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Cloud One Migration Duration And Its Drivers, Grayson T. Hall Mar 2025

Cloud One Migration Duration And Its Drivers, Grayson T. Hall

Theses and Dissertations

As modern warfare evolves with rapid technological advancements, cloud computing plays a critical role in managing the vast amounts of data required for real-time decision making, as well as enabling seamless organizational access to mission-critical programs and information from around the globe. Recognizing its importance, the Department of Defense (DoD) identified cloud computing as essential for maintaining the military’s technological edge. However, despite cloud computing’s strategic significance, the DoD faces challenges in successfully implementing department-wide cloud computing. In contrast, the Air Force’s cloud computing environment, Cloud One, is fully operational and has already integrated over 145 systems into its platform. …

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Understanding Avionics Maintenance Training Student Outcomes In A Student-Centered Active Learning Environment, Scott M. Wyman Mar 2025

Understanding Avionics Maintenance Training Student Outcomes In A Student-Centered Active Learning Environment, Scott M. Wyman

Theses and Dissertations

This study examines the effects of active learning compared to didactic methodologies on two soft skills, namely teamwork and self-efficacy using regression analyses and connected letter reports. Learning styles and personality traits were used as predictors. Findings indicate significant interaction effects between methodology, aural learning style, and personality traits on self-efficacy and teamwork ability. The findings highlight the nuanced role of learner traits in shaping teamwork outcomes across instructional methods. While active learning supports soft skills, individual differences must be considered in instructional design to optimize teamwork in technical education settings.

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Evaluating Weather Effects On Sortie Generation Using Discrete Event Simulation, Markus Case Mar 2025

Evaluating Weather Effects On Sortie Generation Using Discrete Event Simulation, Markus Case

Theses and Dissertations

United States Air Force (USAF) operations rely on sortie generation, a complex system involving aircraft maintenance, operational planning, munitions, security forces, and aircrew. Failures in any of these areas can jeopardize a mission, and extreme weather events such as lightning, high winds, and snow further complicate operations. This thesis examines the impact of extreme weather on sortie generation, focusing on developing a data-driven discrete-event simulation (DES) to predict generation timelines and identify high-risk areas. The model allows users to adjust key inputs, including the month, number of aircraft, processing times, and personnel/equipment availability. By simulating real-world conditions, the model helps …

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Data Lakehouse And Machine Learning Pipeline For Aircraft Fuel Efficiency Experimentation, Skyler G. Kepley Mar 2025

Data Lakehouse And Machine Learning Pipeline For Aircraft Fuel Efficiency Experimentation, Skyler G. Kepley

Theses and Dissertations

Fuel efficiency is crucial for the U.S. Air Force, impacting mission success, aircraft performance, and cost savings. This study presents an information system that integrates flight and maintenance data using a data lakehouse. It automates ingestion, enrichment, and predictive modeling, leveraging AutoML for optimization and SHAP for transparency. A case study on C-130J aircraft shows that optimizing D Check cycles can save 11.52 pounds of fuel per flight hour. These findings highlight the effectiveness of data-driven decision-making in aviation, offering a scalable, automated solution for improving fuel efficiency and reducing costs.

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Analysis Of An Army Recruiter Allocation Model (Ram), Erik J. Wittendorf Mar 2025

Analysis Of An Army Recruiter Allocation Model (Ram), Erik J. Wittendorf

Theses and Dissertations

The United States Army Recruiting Command’s mission to recruit America’s best and brightest volunteers that can deploy, fight, and win requires an effective distribution of its recruiting force to serve as local community ambassadors. This research analyzes an Army recruiter allocation model (RAM) and assesses its underlying assumptions, objective function, and constraints. A detailed study of relative market potential and production rates for up to 1,319 Army recruiting stations and 18,789 ZIP codes enables RAM modification recommendations leveraging evolving recruiting concepts and identifies areas of future work to continue improving the Army’s understanding of the recruiting environment.

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Cognitive Warfare In The South China Sea: Analyzing Media Influence And Public Opinion In Taiwan, Wyatt F. Blatti Mar 2025

Cognitive Warfare In The South China Sea: Analyzing Media Influence And Public Opinion In Taiwan, Wyatt F. Blatti

Theses and Dissertations

In a time where conflict extends beyond traditional battlefields, cognitive warfare emerges as a powerful tool to influence perceptions and gain strategic advantages. This study investigates China’s cognitive warfare strategies against Taiwan through trend analysis, topic modeling, and sentiment analysis of news media articles from March 2013 to August 2024 to uncover evolving techniques and mitigation efforts. The findings highlight the potential for tracking cognitive campaigns overtime but will require more than news media alone and suggests future research to better understand indicators of cognitive warfare.

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Evaluating The Performance Of Conformal Prediction Generated Uncertainty Sets In Robust Optimization, Zion C. Johnson Mar 2025

Evaluating The Performance Of Conformal Prediction Generated Uncertainty Sets In Robust Optimization, Zion C. Johnson

Theses and Dissertations

Uncertainty is a major challenge in optimization, especially in problems where unpredictable costs impact decision-making. Robust optimization addresses this by modeling uncertainty via uncertainty sets. These sets are then used such that solutions hold under worst-case scenarios, with success depending on the accuracy of the uncertainty sets. This research examines the use of conformal prediction to construct uncertainty sets for RO, an approach that has not been widely explored. We test split and full conformal prediction in a robust optimization minimum cost flow problem, and comparing them to interval-based and normal-based ellipsoidal uncertainty sets. Experiments run across different network structures …

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End-To-End (E2e) Model-Based Systems Engineering (Mbse) Framework, Joshua Adelabu, Bhushan Lohar Mar 2025

End-To-End (E2e) Model-Based Systems Engineering (Mbse) Framework, Joshua Adelabu, Bhushan Lohar

Shelby Hall Graduate Research Forum Presentations

This presentation explores the latest advancements in systems engineering, with a particular focus on Model-Based Systems Engineering (MBSE). It covers key definitions, benefits, applications, and challenges associated with these methodologies, as well as analyzes current frameworks and research methodologies in the field.

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Understanding The Acceptance Of Digital Tools Within An Air Force Environment Using The Utaut2 Model, Britton J. Edwards Mar 2025

Understanding The Acceptance Of Digital Tools Within An Air Force Environment Using The Utaut2 Model, Britton J. Edwards

Theses and Dissertations

This research paper explores factors influencing digital tool adoption in a military context, using a modified UTAUT2 model with the inclusion of Military Status as a moderating factor. The study examines the moderating effects of Military Status on Social Influence towards Behavioral Intention and Behavioral Intention on Use Behavior. Data was collected through a Likert-scale survey from respondents across multiple Department of the Air Force (DAF) organizations. Findings revealed Social Influence had the potential to positively influence Behavioral Intention to use digital tools, but military experience did not significantly moderate this relationship. However, past experience with the legacy tool and …

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End-To-End (E2e) Mbse Framework, Joshua Adelabu, Bhushan Lohar Mar 2025

End-To-End (E2e) Mbse Framework, Joshua Adelabu, Bhushan Lohar

Shelby Hall Graduate Research Forum Posters

Principles of systems engineering (SE) possess great influence. Frameworks are generated to execute and incorporate such principles. Framework can have various architectural constructs and simultaneously consist of diverse concepts. Concepts like modularity, scalability, integration, and the balance between performance, cost, and risk are guided by SE principles as well as the manufacturability of designed systems due to the migration from mass customization to mass personalization resulting in increased complexity, and as a result need to be guided and gives rise to modularity. However, principles are not enough for effective system design and understanding. There is a need for a framework. …

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Advances Towards A Robotic Management Vehicle Suited To Nurse Row Crops To More Efficient Outcomes, Adam Mk Gronewold Mar 2025

Advances Towards A Robotic Management Vehicle Suited To Nurse Row Crops To More Efficient Outcomes, Adam Mk Gronewold

Dartmouth College Ph.D Dissertations

Enhancing agricultural production while reducing input costs remains a central challenge in modern row-crop management. Recent advances in computation, imagery, and sensors are enabling more efficient practices across various agricultural domains, and automation technologies are increasingly available to manage tasks central to perennial crop development. Automation in row-crop agriculture, by contrast, lags behind. This thesis explores utilizing small, unmanned ground vehicles to transform row cropping through the implementation of unconventional, in-season management strategies. The first focus of this work considers improvements to nitrogen fertilization using small, autonomous vehicles. An agronomy experiment in corn assessed the effects of gradually applying nitrogen …

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Proximal Policy Optimization Applied To The Beyond Visual Range Air Combat Maneuvering Problem, Daniel B. Joseph Mar 2025

Proximal Policy Optimization Applied To The Beyond Visual Range Air Combat Maneuvering Problem, Daniel B. Joseph

Theses and Dissertations

Artificial intelligence (AI) grows ever-more important in warfighting. Emerging technologies allow for the use of AI to control aircraft and weapons systems. This research investigates the application of reinforcement learning (RL) through the Proximal Policy Optimization (PPO) algorithm to a two-versus-two (2v2) beyond-visual-range (BVR) air combat maneuvering problem (ACMP). Implemented in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM), the methodology frames the engagement as a Markov decision process, wherein an autonomous RL agent learns continuous control decisions—throttle, pitch, roll, and yaw—under a cooperative communication scheme. A multi-phase curriculum-learning approach facilitates the progressive acquisition of flight stability, weapon deployment, …

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A Case Study N Modeling The Human Behavior Of Basic Fighter Maneuvers Using Mbse, Josiah J. Franklin Mar 2025

A Case Study N Modeling The Human Behavior Of Basic Fighter Maneuvers Using Mbse, Josiah J. Franklin

Theses and Dissertations

As fighter aircraft become more complex and technology, such as autonomy, is introduced, it is essential to anticipate the critical tasks and information pilots need to accomplish their mission with these new systems. Fighter pilots operate in highly demanding situations where the consequences of failure are severe and require their systems to provide the right information for the task. Traditionally, these designs are informed through Critical Task Analyses of existing systems. This research produced a method for modeling critical task analysis and information requirements using model-based systems engineering. The scenario was a fighter aircraft conducting basic fighter maneuvers in a …

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Spatiotemporal Prediction Of Atmospheric Events Through Recurrent Deep Learning Model, Brian W. F. Popick Mar 2025

Spatiotemporal Prediction Of Atmospheric Events Through Recurrent Deep Learning Model, Brian W. F. Popick

Theses and Dissertations

The main contributions of this research is to add to the growing library of literature on the use of deep learning algorithms for the spatiotemporal prediction of dangerous atmospheric and hydrologic phenomena. Specifically, we develop novel attention-based and non-attention-based recurrent neural network frameworks to produce short-range sequential forecasts for lightning and tornado occurrences. Additionally, we introduce methods that account for and include error in the model tuning process to generate more reliable models. Furthermore, we have created a lightweight spatiotemporal tornadic prediction dataset that we plan to make publicly available. The first component of this research develops three novel spatiotemporal …

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The Application Of Decision Analysis Theory For Space Allocation At The Air Force Institute Of Technology, Damian N. Soriano Mar 2025

The Application Of Decision Analysis Theory For Space Allocation At The Air Force Institute Of Technology, Damian N. Soriano

Theses and Dissertations

This thesis investigates the application of Decision Analysis Theory to optimize space allocation at the Air Force Institute of Technology (AFIT). Through a Multi-Objective Decision Analysis (MODA) framework, this study addresses existing methodologies for space allocation in military, academic, and office settings; the rules, limitations, and factors influencing space utilization at AFIT; and approaches to improve office and lab allocations for institutional efficiency and fairness. This research incorporates qualitative and quantitative metrics, including faculty and student data, research outputs, and historical space usage. These findings highlight significant complexities in space allocation, particularly in reconciling administrative and research requirements with structural …

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